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Record W2995455123 · doi:10.1002/cche.10248

Physicochemical properties of enzymatically modified pea protein‐enriched flour treated by different enzymes to varying levels of hydrolysis

2019· article· en· W2995455123 on OpenAlexaff
Dellaney Konieczny, Andrea K. Stone, Darren R. Korber, Michael T. Nickerson, Takuji Tanaka

Bibliographic record

VenueCereal Chemistry · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChemistryPapainSolubilityHydrolysisTrypsinProteolysisPea proteinPepsinChromatographyEnzymatic hydrolysisFood scienceEnzymeBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Background and objectives Air‐classified pea protein‐enriched flour (PPEF) was modified with trypsin, Savinase, papain, and pepsin to achieve 2%–4% and 10%–12% degrees of hydrolysis, and the surface and functional properties of the hydrolyzed products were assessed. Findings Surface hydrophobicity increased from 13.3 to 48.5 A.U. with papain treatment. Surface charge became more electronegative from −12.6 to −19.0 mV with pepsin treatment. However, solubility of hydrolyzed PPEF at all pH values tested (4.0, 7.0, and 10.0), regardless of enzyme treatment, decreased. Low solubility of hydrolyzed proteins negatively impacted solubility‐dependant functional properties: foaming properties and emulsifying properties decreased. However solubility‐independent properties (water‐ and oil‐holding capacity; WHC and OHC, respectively) improved with proteolysis, increasing from 0.6 g/g to 1.4–2.0 g/g and from 0.7 g/g to 1.0–1.5 g/g, respectively. Conclusions Papain treatment yielded the best results in terms of both OHC and WHC of PPEF. Significance and novelty The resultant hydrolyzates have potential for baked goods and processed meat applications because of their increased OHC and WHC.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.207
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations47
Published2019
Admission routes1
Has abstractyes

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